Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 2
Articles | Volume 18, issue 2
https://doi.org/10.5194/gmd-18-433-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-433-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 2
Methods for assessment of models
 | 
27 Jan 2025
Methods for assessment of models |  | 27 Jan 2025

ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool

Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini

Viewed

Total article views: 2,366 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,743 549 74 2,366 80 158
  • HTML: 1,743
  • PDF: 549
  • XML: 74
  • Total: 2,366
  • BibTeX: 80
  • EndNote: 158
Views and downloads (calculated since 18 Apr 2024)
Cumulative views and downloads (calculated since 18 Apr 2024)

Viewed (geographical distribution)

Total article views: 2,366 (including HTML, PDF, and XML) Thereof 2,366 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Dec 2025
Download
Short summary
This paper presents the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a computationally efficient tool that uses machine learning algorithms for sensitivity analysis in atmospheric models. It is tested with the Weather Research and Forecasting (WRF) model coupled with the Noah-Multiparameterization (Noah-MP) land surface model to investigate sea breeze circulation sensitivity to vegetation-related parameters.
Read more
Share